Time-specific niche modeling (TENM) is a novel approach that
allows calibrating niche models with high temporal resolution spatial
information, which aims to reduce niche estimation biases. What makes
the tenm package stand out is its distinctive capability to calibrate
models by incorporating specific temporal information, whether on a
yearly, monthly, or even daily basis. This feature distinguishes it from
traditional models that rely on averaged temporal data.
Some of the package functions are:
- Time-specific spatial data thinning: data cleaning considering the
temporal dimension of records.
- Time-specific environmental data extraction: extract environmental
data from variables considering the temporal dimension of data.
- Time-specific background generation: generate background points for
the modeling process by considering the temporal dimension of the
occurrence and environmental data: The number of background points for
each year is proportional to the number of occurrences for each year of
observation.
- Exporting time-specific information as Samples With Data format:
export the time-specific data to the Samples With Data format table.
This function allows users to use other modeling algorithms such as
MaxEnt and GLMs.
- Time-specific model calibration: calibrate time-specific niche
models using minimum volume ellipsoids. It fits numerous models based on
a combination of user-set parameters, including different combinations
of environmental variables.
- Time-specific model selection: select n models from all the fitted
models using statistical and model performances as model selection
criteria.
- Projecting time-specific niche models: project one or more of the
selected models onto both the environmental and/or geographical
space.